ORIGINAL PAPER

Modeling the effects of saline water use in wheat-cultivated landsusing the UNSATCHEM model

Fatemeh Rasouli • Ali Kiani Pouya •

Jiřı́ Šimůnek

Received: 28 April 2012 / Accepted: 25 July 2012 / Published online: 23 August 2012

� Springer-Verlag 2012

Abstract Waters of poor quality are often used to irrigate

crops in arid and semiarid regions, including the Fars

Province of southwest Iran. The UNSATCHEM model was

first calibrated and validated using field data that were

collected to evaluate the use of saline water for the wheat

crop. The calibrated and validated model was then

employed to study different aspects of the salinization

process and the impact of rainfall. The effects of irrigation

water quality on the salinization process were evaluated

using model simulations, in which irrigation waters of

different salinity were used. The salinization process under

different practices of conjunctive water use was also

studied using simulations. Different practices were evalu-

ated and ranked on the basis of temporal changes in root-

zone salinity, which were compared with respect to the

sensitivity of wheat to salinity. This ranking was then

verified using published field studies evaluating wheat

yield data for different practices of conjunctive water use.

Next, the effects of the water application rate on the soil

salt balance were studied using the UNSATCHEM simu-

lations. The salt balance was affected by the quantity of

applied irrigation water and precipitation/dissolution reac-

tions. The results suggested that the less irrigation water is

used, the more salts (calcite and gypsum) precipitate from

the soil solution. Finally, the model was used to evaluate

how the electrical conductivity of irrigation water affects

the wheat production while taking into account annual

rainfall and its distribution throughout the year. The max-

imum salinity of the irrigation water supply, which can be

safely used in the long term (33 years) without impairing

the wheat production, was determined to be 6 dS m-1.

Rainfall distribution also plays a major role in determining

seasonal soil salinity of the root zone. Winter-concentrated

rainfall is more effective in reducing salinity than a similar

amount of rainfall distributed throughout autumn, winter,

and spring seasons.

Introduction

Waters of poor quality are often used to irrigate crops in

arid and semiarid regions, including the Fars Province in

southwest Iran. It is well known that due to high concen-

trations of soluble salts, the use of such waters may result

not only in the decrease of crop yield, but also in the

reduction in soil water infiltration capacity (e.g., McNeal

1974; Shainberg and Levy 1992; Qadir et al. 2000). A

variety of strategies have been adopted to overcome

problems associated with soil salinity, including improving

the productivity of saline soils mainly through leaching of

excess soluble salts, blending and reusing of saline drain-

age waters, selecting of tolerant varieties of suitable crops,

and using appropriate agronomic practices (Rhoades et al.

1992). Adoption of suitable salinity control measures

requires determination of salt and water movement through

the soil profile and prediction of crop response to soil water

Communicated by J. Ayars.

F. Rasouli (&) � A. Kiani PouyaDepartment of Salinity Research, Fars Research Center

for Agriculture and Natural Resources, Bakhshande Blvd.,

Zarghan, Fars, Iran

e-mail: rasouli@farsagres.ir

A. Kiani Pouya

e-mail: ali.kiani@farsagres.ir

J. Šimůnek

Department of Environmental Sciences,

University of California Riverside, Riverside, CA 92521, USA

e-mail: Jiri.Simunek@ucr.edu

123

Irrig Sci (2013) 31:1009–1024

DOI 10.1007/s00271-012-0383-8

and soil salinity, subject to various climatic, edaphic, and

agronomic factors (Ferrer and Stockle 1999).

Wheat is the main winter cereal crop planted over an

area of almost 600 thousand hectares in the Fars Province.

About 44 % of the total cultivated area in the Fars Province

is cultivated with this crop. Wheat is ranked as a moder-

ately salt-tolerant crop (Maas and Grattan 1999) that can be

safely irrigated with moderately saline water, although an

increase in water salinity may cause a reduction in the

wheat grain yield. However, the wheat yield is also

affected by several other factors, including soil, crop, and

environmental conditions, which interact with soil salinity

to cause different yield responses.

Several methods are available for a thorough assess-

ment of what impact water quality and existing farm

irrigation management may have on soil salinity (e.g.,

Kelleners and Chaudhry 1998; Sharma and Rao 1998). By

far, the most widely used method for salinity assessment

is detailed soil sampling and subsequent laboratory mea-

surement of salinity (Cheraghi et al. 2007). However, the

traditional soil salinity assessment method requires con-

siderable time, expense, and effort and cannot fully cover

the spatial and temporal patterns of variability at the field

scale. There has been considerable success in using

ground-based geophysical measurements of the apparent

soil electrical conductivity (ECa) to assess salinity across

individual fields, including the electrical resistivity (ER)

or electromagnetic induction (EM) surveys (e.g., Corwin

and Lesch 2003). However, even these methods are cur-

rently too time consuming to be applied cost-effectively

at regional scales (Lobell et al. 2010). Therefore, there is

a need for the development of more practical (i.e., quicker

and cheaper) methods and tools to determine and evaluate

soil salinity in order to improve decision-making

processes.

Mathematical models that consider and integrate various

climate, crop, and soil factors have been suggested as

useful tools for assessing the best management practices

for saline conditions (e.g., Ramos et al. 2011, 2012).

Steady-state and transient water flow and solute transport

models are the two main classes of currently available

models for the assessment of salinity management. Steady-

state models, which assume steady-state water flow

through the soil profile and constant soil solution concen-

trations at any point of the root zone at all times, are not

suitable for irrigated lands under saline conditions (Letey

and Feng 2007; Corwin et al. 2007; Letey et al. 2011). A

large number of transient flow and transport models,

including UNSATCHEM (Šimůnek et al. 1996), SWAP

(van Dam et al. 1997), HYDRUS-1D (Šimůnek et al.

2008), and HYSWASOR (Dirksen et al. 1993), among

many others, have been developed to simulate integrated

effects of climate, soil, and plants.

UNSATCHEM is a transient flow and transport model

that considers the effects of many variables and factors, such

as the initial soil salinity, time and amount of rainfall, water

quality, and blending of saline and fresh waters (Bradford

and Letey 1992; Kaledhonkar and Keshari 2006; Gonçalves

et al. 2006; Ramos et al. 2011, 2012; Kaledhonkar et al.

2012). The UNSATCHEM model can additionally account

for chemical reactions, such as aqueous complexation, cation

exchange, and salts precipitation and/or dissolution, which

are important for assessing the effects of the quality of irri-

gation water in arid and semiarid regions.

The UNSATCHEM model is a suitable tool for studies

evaluating salt movement in soils irrigated with brackish

waters because it can accurately predict not only soil water

contents and concentrations of soluble ions, but also inte-

gral variables, such as electrical conductivity, the sodium

adsorption ratio, and sodium exchange percentage (Gon-

çalves et al. 2006; Ramos et al. 2011). For example, Suarez

et al. (2006) modeled the effects of rainfall on the per-

meability of a soil with a high sodium content. Their

simulation results demonstrated that for regions where

rainfall is significant, the Na hazard is considerably greater

than what would be suggested by a simple application of

commonly used EC–SAR hazard relationships.

Studies assessing soil salinity using numerical models

are very limited in Iran. For example, Vaziri (1995) eval-

uated four desalinization models in a field study conducted

in two regions of Rudasht and Kangavar. Four different

types of models were compared: (1) a serial reservoir

model, (2) a theoretical plat-thickness model, (3) a

numerical convection–dispersion model, and (4) a numer-

ical model that considered both continuous and intermittent

leaching processes. Results showed that the highest accu-

racy was achieved in both regions and for both conditions

using the convection–dispersion model. Droogers et al.

(2000) used the SWAP model to evaluate field experi-

mental data from the Isfahan Province. They concluded

that the SWAP model can successfully predict water and

salt balances in the root zone for different irrigation man-

agement scenarios with steady-state conditions.

Considering the importance of using numerical models

for a better understanding of salinity processes and for

evaluating various salinity management options, the UN-

SATCHEM model was, in the present study, first calibrated

and validated against the experimental data involving the

use of saline water for irrigating wheat (Wahedi 1995).

Next, further simulations were carried out using the UN-

SATCHEM model to obtain better understanding about

strategies involving the conjunctive use of waters of dif-

ferent qualities, to evaluate the effects of different irriga-

tion water qualities, rainfall amounts, and saline waters and

to develop the guidelines for the management of salinity in

wheat-cultivated lands.

1010 Irrig Sci (2013) 31:1009–1024

123

Materials and methods

Model description

UNSATCHEM is a numerical model for simulating

movement of water, heat, carbon dioxide, and solute in

one-dimensional, variably saturated media (Šimůnek et al.

1996; Šimůnek and Suarez 1994a, b). The model numeri-

cally solves the Richards equation for water flow and

convection–dispersion type equations for heat, carbon

dioxide, and solute transport. The flow equation incorpo-

rates a sink term to account for water uptake by plant roots:

ohot¼ o

ozKh

oh

ozþ Kh

� �� S ð1Þ

where h (L3 L-3) is the volumetric water content, t (T) is time,Kh (L T

-1) is the unsaturated hydraulic conductivity, h (L) is

the soil water pressure head, z (L) is the vertical spatial

coordinate, and S (T-1) defines the root water uptake term.

Root water uptake is simulated as a function of depth and

time, and is a function of the pressure and osmotic heads to

account for water and salinity stresses, respectively.

Transport of seven major ions, namely Ca2?, Mg2?,

Na?, K?, HCO3-, SO4

2-, and Cl-, is considered by UN-

SATCHEM. Solute transport of each aqueous species is

simulated using the advection–dispersion equation:

ohckotþ qb

o�ckot¼ o

ozDh

ock

oz� qck

� �k ¼ 1; . . .; 7 ð2Þ

where ck (M L-3) is the total dissolved concentration of

aqueous species k, ck (M M-1) is the concentration asso-

ciated with the solid phase (i.e., sorbed and precipitated), qb(M L-3) is the soil bulk density, D (L2 T-1) is the dispersion

coefficient, and q (L T-1) is the Darcy water flux. The

transport equation is solved in UNSATCHEM using a

Galerkin finite-element method (van Genuchten 1987).

The UNSATCHEM model accounts for equilibrium

chemical reactions between major ions, such as aqueous

complexation, cation exchange, and precipitation–dissolu-

tion (Šimůnek and Suarez 1994a, b). Activity coefficients are

determined using either modified Debye–Huckel or Pitzer

equations to calculate single-ion activities. All cations in the

solution are assumed to be in equilibrium with the sorbed

cations, which balance the negatively charged sites of the soil.

Cation exchange is a dominant chemical process for the major

cations in the solution in the unsaturated zone (Suarez 2001).

The UNSATCHEM model uses a Gapon-type expression to

describe exchange equilibrium between the sorbed and dis-

solved cations (White and Zelazny 1986):

Kij ¼�cbþi ðcaþj Þ

1=a

�caþj ðcbþj Þ1=b

ð3Þ

where b and a are the valences of species i and j,

respectively, and Kij is the Gapon selectivity coefficient

(dimensionless). Parentheses indicate ion activity

(dimensionless), and �c in (3) is the concentration of

adsorbed cations (mmolc kg-1).

The model considers a set of solid phases, including

calcite (CaCO3), gypsum (CaSO4�2H2O), and others. Themodel allows for precipitation of a mineral whenever the

product of molar concentrations of their constituent ions

raised to the power of their respective stoichiometric

coefficients exceeds the Ksp value of the mineral (Šimůnek

and Suarez 1994a, b).

Root water uptake is simulated as a function of depth and

time, and is a function of the pressure and osmotic heads to

account for water and salinity stresses, respectively:

Sðh; pÞ ¼ a1ðhÞa2ðpÞSp ð4Þ

where Sp is the potential water uptake rate (L3 L-3 T-1) in

the root zone, a1(h) and a2(p) are the pressure and osmoticstress response functions (dimensionless), respectively, and

p is the osmotic head (L).The S-shaped stress response functions proposed by van

Genuchten (1987) were used to evaluate the reduction due to

pressure and salinity stresses for the rate of water extraction.

Reduction functions a1(h) and a2(p) for pressure and salinitystresses are described by the following pair of functions:

a1ðhÞ ¼1

1þ hh50

� �p ð5Þ

a2ðpÞ ¼1

1þ pp50� �p ð6Þ

where h50 and p50 are the pressure and osmotic heads at whichthe water extraction rates are reduced by 50 % during con-

ditions of negligible osmotic and pressure stress, respec-

tively, and p (dimensionless) is an empirical constant.

Field experiments

The data for calibration and validation of the model were

obtained from a research project reported by Wahedi

(1995). The field experiment was conducted in Sarvestan,

Fars Province (29.12�N, 53.12�E), to study the effects ofthree irrigation water salinities (2.5, 6.5, and 11.5 dS m-1)

and three leaching requirements corresponding to 10, 25,

and 50 % reductions in the grain yield of winter wheat

(namely LR10 %, LR25 %, and LR50 %). The experiment

was conducted in a randomized block design with four

replications during three consecutive years (1991–1994).

Seeds were planted on November 28 and grain was har-

vested on June 25, 27, and 21 in the first, second, and third

year of the study, respectively.

Irrig Sci (2013) 31:1009–1024 1011

123

Irrigation was provided using the basin irrigation sys-

tem. Two irrigations (with a total of 210 mm) were applied

before the vegetative cover reached 50 % during the winter

season in the first 2 years of the study. In the third year of

the experiment, an additional irrigation was provided in

this growth stage (with a total of 310 mm). Six additional

irrigations were carried out afterward, with a total of

480 mm plus the amount of leaching required to obtain 10,

25, and 50 % yield reductions as calculated using the

leaching requirement equation:

LR ¼ ECiw5EC�e � ECiw� � ð7Þ

where LR is the leaching requirement, ECiw (dS m-1) is the

electrical conductivity of irrigation water, and ECe* (dS

m-1) is the salinity level, at which a particular yield loss is

obtained. ECe* values that resulted in 10, 25, and 50 %

yield reductions in wheat were 7.4, 9.5, and 13 dS m-1,

respectively, according to the Maas and Hoffman equation

(1977). Leaching requirement, total applied water at dif-

ferent ECiw and desired yield reductions during 3 years of

the field experiment are presented in Table 1.

Water samples were analyzed for sodium (Na?), mag-

nesium (Mg2?), calcium (Ca2?), potassium (K?), bicar-

bonate (HCO3-), carbonate (CO3

2-), chloride (Cl-),

sulfate (SO42-), and water electrical conductivity (ECiw).

The electrical conductivity of water samples was measured

in the field. Na? and K? concentrations were measured

using the emission flame photometer. Ca2? and Mg2? were

measured by EDTA titrimetry. HCO3- was determined

using titration with HCl and Cl- was titrated by silver

nitrate, while SO42- was obtained using the gravimetric

method (Richards 1954). The chemical composition of

irrigation waters is presented in Table 2. The calcium

carbonate content of soil was determined by treating 0.5 g

of dried soil with HCl, followed by back titration of

unreacted acid with NaOH (Loeppert and Suarez 1996).

Cation-exchange capacity (CEC) and exchangeable cations

were measured according to Amrhein and Suarez (1990).

The hydrometric method was used to determine the soil

texture (Bouyoucos 1962). The soil texture was clay loam

between 0 and 50 cm depth with a high percentage of

calcium carbonate and a low value of the organic carbon.

The soil is classified as Carbonatic, Thermic, Typic Cal-

cixerepts according to soil taxonomy (Soil Survey Staff

1999). Soil texture, calcium carbonate content, cation-

exchange capacity, and Gapon’s selectivity coefficients

were measured before experiment. To assess soil salinity,

soil samples were taken from 0–10, 10–30, and 30–50 cm

depths three times during the wheat growing season every

year over 3 years of the experiment. Averaged salinity

during the season was reported for each depth.

Daily meteorological data were obtained from the Sar-

vestan Weather Station. Total annual rainfall was 309, 276,

and 67 mm during the first, second, and third year of the

experiment, respectively. Reference evapotranspiration,

ETo, was estimated using the FAO Penman–Monteith

method (Allen et al. 1998). Crop evapotranspiration was then

calculated as the product of ETo and crop coefficient (Kc)

using the AQUACROP model. Crop parameters required for

calculating actual evapotranspiration were adopted from

Najafi Mirak (2008). The Sarvestan region has two distinct

weather periods: a cool, rainy season (winter) from

November to April (with about 97 % of the total rainfall) and

a hot, dry season (summer) from May to October (with only

about 3 % of the total rainfall). Required climatic parameters

(ET and rainfall), soil properties, and initial concentrations

for simulations are presented in Table 3.

Table 1 Leaching requirement, total applied water at different ECiw, and desired yield reductions during 3 years of field experiment

ECiw(dS m-1)

Desired yield

reduction (%)

Soil salinity level

at which a desired

yield reduction

is achieved (dS m-1)

Applied water

before vegetative

cover reached

50 % (mm)

Crop water

requirements

(mm)

Leaching

requirements

(mm)

Total applied

water (mm)

2.5 10 7.4 210a (310)b 480 34.8 725a (825)b

2.5 25 9.5 210 (310) 480 26.7 717 (817)

2.5 50 13 210 (310) 480 19.2 709 (809)

6.5 10 7.4 210 (310) 480 102.3 792 (892)

6.5 25 9.5 210 (310) 480 76.1 766 (866)

6.5 50 13 210 (310) 480 53.3 743 (843)

11.5 10 7.4 210 (310) 480 216.5 906 (1,006)

11.5 25 9.5 210 (310) 480 153.3 843 (943)

11.5 50 13 210 (310) 480 103.2 793 (893)

a Applied water in the first 2 years, b Applied water in the third year

1012 Irrig Sci (2013) 31:1009–1024

123

The UNSATCHEM model setup

The UNSATCHEM simulations were conducted using the

atmospheric boundary condition (BC) with surface runoff.

However, the surface runoff option was insignificant since

specified daily precipitation and irrigation fluxes were

lower than the infiltration capacity of the upper soil layer.

The upper BC consisted of daily values of rainfall and

potential evapotranspiration, which was partitioned into

potential transpiration and potential evaporation using the

modified approach of Ritchie (1972). The AQUACROP

model was applied to calculate soil evaporation and crop

transpiration separately based on the fraction of green

canopy ground cover (Raes et al. 2009).

Free drainage BC was used at the bottom of the soil

profile. The root growth was modeled using a logistic

growth function, assuming the initial root growth time set

at the planting date and 50 % root growth halfway through

the growing season. Parameters for the van Genuchten

model (van Genuchten 1980) describing soil hydraulic

properties were derived from soil textural information and

the soil bulk density using the ROSETTA program (Schaap

Table 2 Chemical composition of irrigation water

EC

(dS m-1)

Ca2?

(meq L-1)

Mg2?

(meq L-1)

Na?

(meq L-1)

K?

(meq L-1)

HCO3-

(meq L-1)

SO42-

(meq L-1)

Cl-

(meq L-1)

2.5 5.5 5.0 11.9 0.4 3.7 5.8 12.5

4.0 11.2 12.4 17.1 0.5 1.1 11.3 28.2

6.0 18.5 18.0 22.1 0.5 4.1 17.0 37.2

6.5 18.5 18.0 27.1 0.5 4.1 17.0 46.2

8.0 19.4 21.3 49.3 0.5 5.5 28.5 54.9

10.0 21.1 31.7 54.3 0.4 4.4 34.3 61.4

11.5 21.0 29.1 60.9 0.4 3.9 22.5 92.0

Table 3 Parameter values used in the UNSATCHEM model

Soil parameter Value Unit

Soil classification Carbonatic, Thermic, Typic Calcixerepts –

Soil texture Clay loam –

Soil bulk density 1.35 g cm-3

Soil hydraulic parameters

Ks 6.24 cm day-1

a 0.019 cm-1

n 1.31 –

hr 0.095 –

hs 0.41 –

Cation-exchange capacity 165 mmolc kg-1 soil

Gapon’s selectivity coefficients

Ca–Na exchange 10.3

Ca–Mg exchange 0.92

Ca–K exchange 0.41

Initial dissolved concentrations –

In plots irrigated with ECiw = 2.5 and LR10 %, LR25 %, and LR50 % 3.7, 4.2, 3.7 dS m-1

In plots irrigated with ECiw = 6.5 and LR10 %, LR25 %, and LR50 % 9.4, 8.9, 8.2 dS m-1

In plots irrigated with ECiw = 11.5 and LR10 %, LR25 % and LR50 % 13.9, 13.8, 15.7 dS m-1

Initial sorbed concentrations (Ca; Mg; Na; �K) 105, 35, 10, 15 mmolc kg-1 soil

Calcite 400 gr kg-1

Potential ET (1st, 2nd, and 3rd year) 646, 695, 686 mm

Long-term mean potential ET 635 mm

Crop coefficient (initial, developing, and ripening stages) 0.51, 1.05, 0.4

Rainfall (1st, 2nd, and 3rd year) 309, 276, 67 mm

Long-term mean rainfall 250 mm

Irrig Sci (2013) 31:1009–1024 1013

123

et al. 2001). Since field data related to CO2 concentrations

(cm3 cm-3), fluxes, and production were not available, the

CO2 concentrations were assumed to increase linearly from

0.00033 (the CO2 concentration in the atmosphere) at the

soil surface to 0.022 at the 50 cm soil depth.

The relationship between the relative yield of wheat (the

Ghods cultivar) and the mean value of soil salinity during

the growing season for 3 years of experiment is illustrated

in Fig. 1. The first segment of the relationship shows no

change in the relative yield up to a soil salinity threshold of

about 6.9 dS m-1. The second segment is given by the

following equation:

Yr ¼ �0:0561ECe þ 1:388 R2 ¼ 0:92 ð8Þ

where Yr is the relative yield (the actual wheat yield divi-

ded by the maximum wheat yield) and ECe is the average

root-zone salinity in dS m-1. This equation provides the

standard values of threshold and slope for yield reduction

due to salt stress (Maas and Hoffman 1977) of 6.9 dS m-1

and 5.61 % decrease per 1 dS m-1 increase above the

threshold, respectively.

Note that the threshold value obtained in our study

indicated a slightly higher salt tolerance than reported by

Maas and Grattan (1999) (i.e., 6 dS m-1) and lower than

recorded by Francois et al. (1986) (8.6 dS m-1). In

experiments with different wheat cultivars in an extremely

dry region of Iran (the Yazd province), Ranjbar (2005)

found that wheat cultivars were less salt tolerant than in

temperate regions. A threshold of 5.92 dS m-1 and slope of

4.5 % decrease per 1 dS m-1 for a Ghods cultivar were

proposed. According to the results obtained from 150 farms

in 15 provinces in the north, south, and central parts of

Iran, the soil salinity threshold, above which wheat yield

started to decline, was found to be 6.8 dS m-1 (Siadat and

Saadat 1998). Differences between different studies can be

attributed to different experimental conditions, including

genotypes, soils, climate, and/or agronomic practices.

The relationship between the electrical conductivity and

the osmotic pressure head is given by:

p ¼ �3:580ECþ 1:366 R2 ¼ 0:996 ð9Þ

where p has units of meters and EC has units of dS m-1.Equation (9) was determined using UNSATCHEM (Šimůnek

et al. 1996) to compute the osmotic head p for the solutioncomposition of irrigation water reported by Wahedi (1995).

In the UNSATCHEM model, the water and salinity

stresses on root water uptake are defined using the empir-

ical parameters h50 and p50. These parameters represent thepressure and osmotic heads, at which the water extraction

rate is reduced by 50 % due to water and salinity stresses,

respectively. Dehghanian (2004) reported h50 for wheat of

-29.7 m. According to Wahedi (1995), the soil salinity

that causes 50 % loss in the wheat yield is 15.82 dS m-1.

According to Eq. (9), this corresponds to the osmotic

pressure head p50 of -55.3 m.Data from experimental plots with irrigation water with

the salinity of 2.5 dS m-1 were used for model calibration

and that of 6.5 and 11.5 dS m-1 for model validation. For

both calibration and validation, simulations were carried

out for three leaching requirements in 3 years. Simulated

salinities at depths of 0–10, 10–30, and 30–50 cm, aver-

aged over 3 years, were compared with measured salinities

at corresponding depths. Three-year average soil salinities

were used for calibration and validation based on prior

recommendations (e.g., Gan et al. 1997) that 3–5 years of

data that include average, wet, and dry years should be

used for model calibration and evaluation.

Statistical evaluation

Modeling results were evaluated using both graphical and

statistical methods. In the graphical approach, measured

values of soil salinity were plotted against simulated val-

ues. Agreement between simulated and observed values

was evaluated using the root mean square error (RMSE)

and the relative error (RE), which are defined as follows

(Kobayashi and Salam 2000):

RMSE ¼ 1n

XSimi � Obsið Þ2

� 0:5ð10Þ

RE ¼ RMSEObsavg

� ð11Þ

where Simi and Obsi are simulated and observed values,

respectively; n is the number of data points included in the

comparison; and Obsavg is the mean observed value. RE

was used to evaluate the quality of the RMSE value. The

simulation is considered to be excellent when a relative

error is less than 10 %, good if it is greater than 10 % and

less than 20 %, fair if it is greater than 20 % and less than

30 %, and poor if it is greater than 30 % (Loague and

Green 1991).

y = -0.0561x + 1.3485

R2 = 0.9229

0

0.2

0.4

0.6

0.8

1

1.2

2 4 6 8 10 12 14

Average Root-zone Salinity (dS m-1)

Rel

ativ

e Y

ield

Fig. 1 Relationship between the relative wheat grain yield and theaverage root-zone salinity

1014 Irrig Sci (2013) 31:1009–1024

123

Alternative irrigation scenarios

Three alternative scenarios were considered when evalu-

ating the effects of different irrigation waters on the

development of salinity using UNSATCHEM. While no

rainfall was considered in the first scenario, the other two

scenarios considered daily rainfall data. In all alternative

simulations, the longitudinal dispersivity of 9 cm obtained

by calibration against the field data was used.

Scenario 1 Temporal changes in the quality of irrigation

water can significantly influence the salinization process.

These changes can occur when farmers get an occasional

supply of good quality canal water. In such a situation,

saline and canal waters can be used either alternately or

mixed (blended), depending on the availability of good

quality water. The effects of various options involving

either an alternative use of different waters or their

blending on soil salinity were investigated using the cali-

brated UNSATCHEM model. In this scenario, the wheat

crop was considered and rainfall events were neglected.

Simulations were carried out for six sub-scenarios: for two

cases when only either low–salinity (LS) or high-salinity

(HS) waters were used, and for four modes of conjunctive

use (Table 4), namely LS:HS (alternating irrigations with

low- and high-salinity waters, starting with low-salinity

water), HS:LS (alternating irrigations with low- and high-

salinity waters, starting with highly saline water), 3LS:3HS

(initial three irrigations with low-salinity water followed by

three irrigations with highly saline water), and Mix (1:1),

where LS and HS stands for low and high salinity,

respectively. Eight irrigations, each of 9 cm, were applied

in each simulation. UNSATCHEM was run with daily

values of long-term mean potential evapotranspiration. The

EC of irrigation water of 2 (LS), 6 (Mix), and 10 (HS) dS

m-1 was used.

Scenario 2 In lands cultivated with wheat in the Fars

Province, about 35–45 % of applied water percolates

below the root zone into deeper soil layers, leaching salts

and increasing the wheat yield even when high-salinity

irrigation waters are used (Cheraghi and Rasouli 2008).

However, applying large amounts of water is not sustain-

able in arid and semiarid areas (such as in Iran and spe-

cifically in the Fars Province). Therefore, optimizing the

leaching fraction while preserving the crop yield and

minimizing the amount of irrigation water is of primary

interest. This scenario involved four practical irrigation

treatments (1.1ET, 1.2ET, 1.3ET, and 1.4ET, where ET is

the potential evapotranspiration) while considering or

ignoring annual rainfall. Water was applied at one-day

intervals, such as with a drip irrigation system. The use of

pressurized irrigation (e.g., sprinkler or drip irrigation) is

one of the goals of sustainable agriculture in Iran. Long-

term mean atmospheric data (potential ET and rainfall) and

an ECiw of 6 dS m-1 were considered in this scenario.

Other input data required for simulations are given in

Table 3. For each simulation, various salt balance com-

ponents, including leaching, precipitation (of calcite and

gypsum), and final soil salinity were calculated for the soil

depth of 0–50 cm.

Scenario 3 This scenario was carried out to evaluate the

maximum electrical conductivity of irrigation water (ECiw)

that can maintain an average root-zone salinity of less than

6.9 dS m-1, while considering annual rainfall and evapo-

transpiration. Three 3-year time series of rainfall, repre-

senting dry, average, and above-average rainfall years,

were analyzed. Irrigation waters with ECiw of 4, 6, 8, and

10 dS m-1 were tested.

The historical rainfall record over the past 33 years for

the Sarvestan area was obtained from the Meteorological

statistics of the Fars province (2011). Long-term average

precipitation calculated over a time period from 1978 to

2011 was considered a normal year. The year with annual

rainfall either 50 % below or above the long-term mean

was assumed to be a dry or wet year, respectively. Annual

rainfall values were then sorted from the driest year to the

wettest year. If the rainfall of the ith year in the ordered

series was Pi, then the probability that rainfall was lower

than Pi was given by the order number (i) of a year divided

by the total number of years (n) plus 1 [Prob(P B Pi) = i/

(n ? 1)]. The rainfall distribution of the ith year was used

to determine results with Pi probability.

To evaluate the influence of rainfall distribution, two

rainfall records with similar annual rainfall averages but

different distributions throughout the year were selected.

Two years (2005–2006 and 2010–2011) were compared.

Total annual rainfall was almost the same (207 mm) in

both years. In 2005–2006, rainfall was concentrated

in winter (87 % of rainfall occurred from November to

March), whereas in 2010–2011, rainfall was more uni-

formly distributed throughout the winter and spring (only

38 % occurred from November to March). Irrigation

water was assumed to have a salinity of 6 dS m-1 in the

model.

One additional scenario was carried out to evaluate how

particular irrigation water affects the wheat yield over the

long term. In this scenario, a 33-year record of historical

daily rainfall was considered to determine the effect of

irrigation waters with different ECiw on the average root-

zone salinity. In this scenario, the quantity of irrigation

water was assumed to be 120 % of the potential evapo-

transpiration. The simulation was run for 33 years and the

simulated series of soil salinity was used to establish the

probability of obtaining values below or above the

threshold value of soil salinity.

Irrig Sci (2013) 31:1009–1024 1015

123

Results and discussion

Calibration and validation of UNSATCHEM

for modeling the use of saline water for irrigation

During model calibration, the longitudinal dispersivity was

fitted using the trial-and-error method until the simulated

and measured soil salinities agreed within acceptable lim-

its. Good agreement between the simulated and measured

data was obtained by adjusting the dispersivity value to

9 cm, which is in the range of values suggested by many

authors (e.g., Leijnse et al. 1996; Bejat et al. 2000; Van-

derborght and Vereecken 2007).

Vanderborght and Vereecken (2007) wrote a review on

dispersivities for solute transport modeling in soils (the

data base contains 635 entries derived from 57 publica-

tions). Data are provided for a wide range of transport

distances, flow rates, soil textures, pore water velocities,

and scales of experiments (field, column, or core). Dis-

persivity values ranged between 0.1 and 481.1 cm. Dis-

persivities obtained for conditions most similar to our study

(a relatively heavy soil texture, field condition, and travel

distance of 50 cm) were in the range from 2.7 to 13.5 cm.

We chose the minimum, maximum, and mean dispersivity

values for inverse simulations. In each model run, simu-

lation results were compared against the measured soil

salinity data. Differences between measured and simulated

values were evaluated by means of the RMSE. Differences

between measured and simulated values were minimized

with a dispersivity of 9 cm. Therefore, this value was used

for remaining simulations in the present study. A com-

parison between measured and simulated soil salinities for

experimental treatments with irrigation water with a

salinity of 2.5 dS m-1 and three different leaching

requirements is depicted in Figs. 2 and 3.

The RMSE of 0.34 dS m-1 (11.2 % relative error)

shows that the UNSATCHEM model is able to predict soil

salinities with an acceptable accuracy. Graphical display of

results for the calibration (Fig. 3) and validation (Fig. 4)

periods indicates that the UNSATCHEM model adequately

described experimental data, although the calibration

results showed a better match than the validation results.

RMSE values for the calibration period ranged from 0.31 to

0.38, while RE ranged from 10.5 to 12.3 % (Fig. 5). RMSE

values ranged from 0.52 to 1.01 dS m-1 and from 1.60 to

2.13 dS m-1 for the validation period with water salinities

of 6.5 and 11.5 dS m-1, respectively. UNSATCHEM

predicted soil salinity with relative errors of 10.6 and

14.9 % when using irrigation water with medium- and

high-level salinity, respectively.

Higher differences between simulated and measured

salinities for experiments with the highest salinity of irri-

gation water (11.5 dS m-1) indicate that the model per-

forms better for lower salinity levels. However, RE

remained in the range of 10 to 20 %, which, according to

evaluation guidelines, indicates a good accuracy of the

model simulation. This result suggests that UNSATCHEM

provides reasonably accurate predictions of soil salinity for

different ranges of water quality and quantity. The

RMSE= 0.34 dS m-1

RE= 11.2%

0

1

2

3

4

0 1 2 3 4

Measured Soil Salinity(dS m -1)

Sim

ulat

ed S

oil S

alin

ity

(dS

m-1

)

LR10%

LR25%

LR50%

Fig. 2 Comparison between measured and simulated (with theUNSATCHEM model) soil salinities for experimental treatments

with irrigation water with salinity of 2.5 dS m-1 and three different

leaching requirements (LR10 %, LR25 %, and LR50 % are leaching

requirements for 10, 25, and 50 % yield reductions, respectively)

0

10

20

30

40

50

60

Soil Salinity (dS m-1)

Soil

Dep

th (

cm)

Measured

Simulated

ECiw= 2.5 dS m-1

LR10%

0

10

20

30

40

50

60

Soil Salinity (dS m-1)

ECiw= 2.5 dS m-1

LR 25%

0

10

20

30

40

50

60

0 5 10 15 20 0 5 10 15 20 0 5 10 15 20

Soil Salinity (dS m-1)

ECiw= 2.5 dS m-1

LR 50%

Fig. 3 Graphical comparisonbetween measured and

simulated soil salinities for

experimental treatments with

irrigation water with salinity of

2.5 dS m-1 and three different

leaching requirements (LR10 %,

LR25 %, and LR50 % are

leaching requirements for 10,

25, and 50 % yield reductions,

respectively)

1016 Irrig Sci (2013) 31:1009–1024

123

UNSATCHEM model can thus be used to evaluate the

effects of various factors on the development of salinity for

different situations.

Scenario 1: Salinization for different modes

of conjunctive water use

Different scenarios (Table 4) with cyclic and blending use

of irrigation water were evaluated using the UNSATCHEM

model. The average root-zone salinity throughout the sea-

son for the LS:HS, HS:LS, 3LS:3HS, and mixed (1:1)

modes were 6.31, 5.93, 6.04, and 5.56 dS m-1,

respectively. The lowest soil salinity was obtained for the

mixed (1:1) mode and the highest for the LS:HS mode.

Since the salt tolerance of wheat varies at different

stages of its growth, seasonal changes in root-zone salinity

can influence the crop yield substantially. Time changes of

the average root-zone salinity for different modes of con-

junctive water use are shown in Fig. 6. For the 3LS:3HS

mode, soil salinity remained lower than 6.9 dS m-1 (the

threshold limit for yield reduction) up to 120 days after

planting. Although for this mode, soil salinity was rela-

tively high during the last 40 days of the growth period (up

to 11.2 dS m-1), salinity during this growth phase does not

0

10

20

30

40

50

60

0 5 10 15 20

Soil Salinity (dS m-1)

Soil

Dep

th (

cm)

MeasuredSimulated

ECiw= 6.5 dS m-1

LR10%

0

10

20

30

40

50

60

Soil Salinity (dS m-1)

ECiw= 6.5 dS m-1

LR25%

0

10

20

30

40

50

60

Soil Salinity (dS m-1)

ECiw= 6.5 dS m-1

LR50%

0

10

20

30

40

50

60

Soil

Dep

th(c

m)

ECiw= 11.5 dS m-1

LR 10%

0

10

20

30

40

50

60

ECiw= 11.5 dS m-1

LR 25%

0

10

20

30

40

50

60

ECiw= 11.5 dS m-1

LR 50%

0 5 10 15 20 0 5 10 15 20

0 5 10 15 200 5 10 15 200 5 10 15 20

Fig. 4 Validation of theUNSATCHEM model for

experimental treatments with

salinities of irrigation water of

6.5 and 11.5 dS m-1 and three

different leaching requirements

(LR10 %, LR25 %, and LR50 %are leaching requirements for

10, 25, and 50 % yield

reductions, respectively)

Table 4 Results simulated using UNSATCHEM for different modes of the conjunctive water use (Scenario 1)

Conjunctive

use practice

Description Soil salinity at

different depths

(dS m-1)

Average root-

zone

salinity

(dS m-1)0–30 30–60 60–90

LS Irrigations with low-salinity water only 3.52 3.88 4.53 3.97

3LS ? 3HS Three irrigations with low-salinity water, followed by three irrigations

with high-salinity water

4.88 5.08 8.17 6.04

LS ? HS Alternate irrigations with low-salinity and high-salinity waters 4.28 5.85 8.82 6.31

HS ? LS Alternate irrigations with high-salinity and low-salinity water 4.27 5.19 8.33 5.93

MIX (1:1) Irrigations with water mixed from low- and high-salinity waters (1:1) 4.26 5.27 8.17 5.56

HS Irrigations with high-salinity water only 7.73 8.50 13.31 9.85

Irrig Sci (2013) 31:1009–1024 1017

123

have a considerable effect on wheat’s function and perfor-

mance. For the LS:HS mode, soil salinity was low during

the first 35 days. After that, soil salinity started fluctuating

between 4.9 and 6.9 dS m-1, depending on whether good-

or poor-quality water was used for irrigation. For the HS:LS

mode, high-salinity water was applied during the early stage

of the wheat growth. Although soil salinity was then

reduced when high-quality water was used for irrigation

later on, this practice should not be used because early

salinity may adversely affect germination, a growth stage

negatively affected by high salinity. For the mix (1:1) mode,

soil salinity fluctuated around 5.38 dS m-1 from the

beginning to the end of the growing season.

The early stages of wheat growth are considered most

sensitive to the salt stress, and its salt tolerance increases

with growth (Maas and Poss 1989; Ranjbar 2010). In order

to minimize the salinity stress, good quality water must be

allocated for the most sensitive stages of crop growth.

Therefore, the most suitable modes of conjunctive water

use with low- and high-salinity waters during the growing

season, while controlling initial soil salinity, are 3L:3H,

followed by the LS:HS, and mix (1:1) modes. The optimal

strategy of conjunctive water use for the wheat crop is to

delay as much as possible the use of saline waters, which

may lead to the salinity stress, while always keeping salt

concentrations within permissible limits, especially during

early crop stages.

Wheat yields reported by Minhas and Gupta (1993a) and

Ranjbar (2010) validate this recommendation. Experiments

carried out on Iranian wheat cultivars grown in sand con-

taining 200 mM of salt have indicated that the wheat yield

was reduced by a half in treatments that exposed wheat to

salts during early vegetative growth stages, compared to

treatments in which wheat encountered increased salinity

during later reproductive stages (Ranjbar 2010). It was

concluded that the more delayed irrigation with saline

water is, the more water productivity can be achieved.

Experiments of Minhas and Gupa (1993a) showed that

when irrigations with saline water start at jointing, non-

saline/saline waters are used cyclically, and salinity is

increased gradually, wheat yield is higher than in other

treatments. They estimated that salinity (expressed as

EC50) during the salt-tolerant stage (dough to maturity) can

be as much as 1.4 times higher than during the salt-sensi-

tive growth stage (seedling to root crown) (i.e., 13.2 vs.

9.3 dS m-1). Simulation results indicate that up to 90 % of

the potential wheat yield can be achieved under conjunc-

tive water use when non-saline water is used until the third

post-sowing irrigation, while water with an ECiw of 51.4 dS

m-1 is used thereafter (Minhas and Gupta 1993b).

The above discussion suggests that initial soil salinity,

quality of irrigation water applied during an early growth

stage, and practices of conjunctive water use have a sig-

nificant impact on temporal changes in root-zone salinity

and that these changes affect the wheat yield.

Scenario 2: Salinization for different amounts

of applied water

The salt balance components for different amounts of irri-

gation water (1.1, 1.2, 1.3, or 1.4 ET) and annual rainfall (no

rainfall or average rainfall) are given in Table 5. The soil

profile salt balance (initial and final mass of salts) is given for

the upper layer of soil (50 cm). The salt balance was sig-

nificantly affected by the amount of applied irrigation water

and the presence of rainfall. Results suggest that salt accu-

mulation was inversely proportional to the quantity of

applied water. The less the irrigation water was applied, the

higher was the salinization risks of soils under irrigation. Soil

salinity increased by 8.6, 23.5, and 58 % when the quantity

of applied water decreased from 1.4 ET to 1.3, 1.2, and 1.1

ET, respectively. The corresponding values when annual

rainfall of 300 mm was considered in simulations were 2.7,

5.5, and 13.7 %, respectively. Since 140 mm of rainfall

(during the growth season) met evapotranspiration needs of

0

0.5

1

1.5

2

2.5

3

LR 10% LR 25% LR 50% LR 10% LR 25% LR 50% LR 10% LR 25% LR 50%

ECiw=2.5 ECiw=6.5 ECiw=11.5

Treatments

RM

SE (

dS m

-1)

0

4

8

12

16

20

RE

(%)

RMSE

RE

Fig. 5 The performance statistics comparing simulated versus mea-sured data (RMSE root mean square error, RE relative error)

0

2

4

6

8

10

12

14

0 20 40 60 80 100 120 140 160 180 200

Day After Planting

Soil

Salin

ity

(dS

m-1

)

Mix(1:1) 3LS+3HS

HS+LS HS+LS

Fig. 6 Seasonal changes in average soil salinity for different modesof conjunctive water use

1018 Irrig Sci (2013) 31:1009–1024

123

the plants, this quantity of water was subtracted from the total

irrigation water used. Thus, the salt input to the soil through

irrigation, as given in Table 5, was reduced by 1.4 kg m-2.

Furthermore, 53 % (160 cm) of rainfall occurred during the

winter months, percolating through soil and leaching salts

below the upper soil layer. Hence, the amount of salt stored in

the soil was reduced by rainfall.

Chemical precipitation/dissolution reactions also affected

the salt balance in the soil profile. Results showed that

because irrigation water is high in gypsum and carbonate

minerals, salts tend to precipitate in the soil profile in all

irrigation treatments. The relative amount of salts that pre-

cipitated varied inversely with the amount of applied water

and/or rainfall. More salts precipitated when less irrigation

water was applied, especially if accompanied by a lack of

rain conditions. For conditions without rainfall, 0.89, 0.83,

0.76, and 0.68 kg m-2 salts were estimated to precipitate for

1.1, 1.2, 1.3, and 1.4 ET irrigation treatments, respectively.

Less salts precipitated when soil received 300 mm of rain-

fall. The amounts of precipitated salts for sub-scenarios with

and without rainfall and for different irrigation quantities

during the growing season are shown in Fig. 7. This figure

shows that the less the water for irrigation was applied, the

earlier the salts started precipitating and the more the salts

precipitated during the entire season. Additionally, during

the rainy season (from 10 to 150 days after planting), no salt

precipitation occurred in the soil profile.

Table 5 shows that salts leaching from the top 50 cm of

soil and the amount of applied water were closely related.

More leaching of salts occurred when more irrigation water

was applied. Under no rainfall conditions, 1.07 and

2.55 kg m-2 of salts were leached below the depth of

50 cm when irrigation was equal to 1.1 and 1.4 ET,

respectively. A fraction of leached salts below the depth of

50 cm to salt input through irrigation was 0.68, 0.61, 0.52,

and 0.36 for irrigation equal to 1.4, 1.3, 1.2, and 1.1 ET.

Fraction of leached salts thus decreased with reduced irri-

gation. A similar trend was also observed for conditions

with rainfall. Salt losses under no rainfall conditions were

higher than under conditions with rainfall. This can be

attributed to the higher quantity of salt input through irri-

gation in the absence of rainfall.

A combination of chemical precipitation and leaching

resulted in favorable root-zone salinity. For sustainable

agriculture, irrigation should be managed such that the salt

precipitation is maintained at high levels without a signifi-

cant reduction in the crop production (Bresler et al. 1982).

Simulated results showed that 26 and 30 % of salts applied

with irrigation water in the 1.2 and 1.1 ET irrigation treat-

ments, respectively, contributed to the precipitation process

when no rain was considered. Salt concentrations (Fig. 8)

exceeded 7 dS m-1 after the 115th and 135th day after

planting, respectively, and were continuously increasing

until the end of the season. When rainfall was considered,

soil salinity for all irrigation treatments remained below 7 dS

m-1 during the growing season. These results indicate that an

application of irrigation water at 1.3 ET (a leaching fraction

of 23 %) would guarantee the optimal wheat yield under dry

conditions (no rainfall) in the study area. On the other hand,

the 1.1 ET irrigation treatment (a leaching fraction of 10 %)

can be successfully used in normal years with average

rainfall of 300 mm.

Scenario 3: Evaluation of the maximum ECiwto maintain the average root-zone salinity

below the soil salinity threshold

The maximum EC of the irrigation water (ECiw) that

maintains an average root-zone salinity below 6.9 dS m-1

during the growing season was evaluated for different rates

of rainfall (for dry, average, and above-average rainfall

years). Simulation results indicate that the seasonal-aver-

age root-zone salinity over the 3-year period for sub-

average rainfall conditions (annual rainfall of 110 mm)

was 4.6, 6.7, 9.1, and 10.1 dS m-1 when the EC of irri-

gation water was 4, 6, 8, and 10 dS m-1 (Fig. 9), respec-

tively. Corresponding root-zone salinities for average

rainfall conditions were 4.6, 6.8, 9.1, and 10.1 dS

m-1—almost the same. Finally, wetter conditions (with

average rainfall of 450 mm) resulted in average root-zone

salinity of 3.2, 4.8, 6.4, and 7.4 dS m-1, respectively. For

all irrigation waters in the above-average rainfall years and

for an ECiw of 4 dS m-1 in average rainfall years, the

seasonal root-zone salinity was lower than the ECiw of

Table 5 Components of the salt balance (kg m-2) for simulations involving wheat crop and various water applications (Scenario 2)

Rain = 0 Rainfall = 300 mm

1.4 ET 1.3 ET 1.2 ET 1.1 ET 1.4 ET 1.3 ET 1.2 ET 1.1 ET

Initial 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

Irrigation 3.74 3.47 3.20 2.94 2.35 2.08 1.81 1.54

Leaching 2.55 2.13 1.67 1.07 1.66 1.24 0.91 0.56

Final 0.81 0.88 1.00 1.28 0.73 0.75 0.77 0.83

Precipitation (calcite and gypsum) 0.68 0.76 0.83 0.89 0.26 0.39 0.43 0.45

Irrig Sci (2013) 31:1009–1024 1019

123

irrigation water, due to rain-induced salt leaching during

the cool winter rainy season.

During sub-average and average rainfall years, irrigation

waters with salinities of 4 and 6 dS m-1 can be safely used

without any loss in the wheat yield. However, irrigation

water salinities of 8 and 10 dS m-1 produced reductions in

wheat yield of 12.1 and 12.4 %, and 18.0 and 18.6 % in

sub-average and average rainfall years, respectively. For

above-average rainfall years, soil salinity remained below

the salinity threshold except when irrigation water with

salinity of 10 dS m-1 was used. However, even this case

did not produce a dramatic reduction in wheat yield.

Reduced grain yield can be attributed to osmotic stress.

In saline soils, osmotic potential can be so low that plants

cannot overcome it and cannot take up enough water,

suffering from the effects of osmotic or salinity stress

(Heikal and Shaddad 1981). Osmotic stress reduces ger-

mination, seedling growth, leaf expansion, and the root

growth rate, leading to a reduction in the yield of the wheat

crop (Läuchli and Grattan 2007).

The following relationships between permissible irri-

gation water salinity (EC*iw) and the wheat salinity

threshold (ECt) for different rainfall series were developed

based on simulated results discussed above:

EC�iw ¼ 1:042ECt � 0:978 R2 ¼ 0:978Sub-average rainfall

ð12Þ

EC�iw ¼ 1:069ECt � 1:083 R2 ¼ 0:975Average rainfall

ð13Þ

EC�iw ¼ 1:306ECt � 0:704 R2 ¼ 0:993Above-average rainfall

ð14Þ

These relationships (Eqs. 12–14) can be used to estimate

the salinity of irrigation water that will not produce average

soil salinity higher that the threshold for wheat production.

Considering a soil salinity threshold of 6.9 dS m-1 and

substituting it into Eqs. 12–14, the maximum salinity of

irrigation water for different rainfall conditions can be esti-

mated. The maximum salinity of irrigation water for the

optimum production of wheat is 6.21 and 6.29 dS m-1 for

sub-average and average rainfall years, respectively. Inter-

estingly, the dryer 3-year period resulted in average root-zone

salinity values equal to those for the average 3-year period,

suggesting that rainfall distribution also plays an important

role in determining the soil salinity. Irrigation water with a

salinity of up to 8.31 dS m-1 can be applied without any

reduction in wheat yield in above-average rainfall years.

These results suggest that consideration of rainfall

would produce lower leaching requirements or higher

permissible ECiw, thus allowing farmers to use either less

irrigation water or water with lower quality (Letey et al.

2011; Isidoro and Grattan 2011). Data collected from the

wheat-cultivated land of the Fars Province indicate that the

average wheat yield increases by about 900 kg ha-1 in

those years, for which annual rainfall is 50 % above its

long-term mean (Cheraghi and Rasouli 2008). Saline water

was also successfully used in the high-rainfall ([550 mm)

0.0

0.2

0.3

0.5

0.6

0.8

0.9

0 20 40 60 80 100 120 140 160 180 200

Salt

Pre

cipi

tati

on (

kg m

-2)

Day After Planting

1.1 ET 1.2 ET1.3 ET 1.4 ETR-1.1 ET R-1.2 ETR-1.3 ET R-1.4 ET

Fig. 7 Changes in salt precipitation during wheat growing season(ET evapotranspiration, R simulation considering annual rainfall)

0

2

4

6

8

10

12

14

0 20 40 60 80 100 120 140 160 180

Soil

Salin

ity

(dS

m-1

)

Day After Planting

1.1 ET 1.2 ET1.3 ET 1.4 ETR-1.1 ET R-1.2 ETR-1.3 ET R-1.4 ET

Fig. 8 Changes in soil salinity during wheat growing season (ETevapotranspiration, R simulations considering annual rainfall)

0

2

4

6

8

10

12

14

Subnormal Normal Above-normal

Rainfall

Ave

rage

Roo

t zo

ne S

alin

ity

(dS

m-1

)

ECiw= 4 dS/mECiw= 6 dS/mECiw= 8 dS/mECiw= 10 dS/m

Fig. 9 Simulated root-zone salinity for various rainfall rates anddifferent qualities of irrigation water

1020 Irrig Sci (2013) 31:1009–1024

123

agro-ecological zone of India. Guidelines for utilizing

saline water in these areas indicate that allowable ECiw is

about 1.8 times larger than that in low-rainfall (\350 mm)regions (Minhas et al. 1998).

Another scenario that was evaluated involved the entire

33-year rainfall series (Fig. 10). Simulations that consider

applications of irrigation waters of different salinities for the

entire 33-year series of meteorological data should provide

the best insight into variations of soil salinity over the long

term. If irrigation waters with an ECiw of 4, 6, 8, and 10 dS

m-1 are used for the entire period of 33 years (from 1978 to

2011), the ECe seasonal-average root-zone salinity will

range from 2.3 to 4.6, 3.9 to 7.0, 4.6 to 9.3, and 5.35 to

11.6 dS m-1, with mean values of 3.9, 5.8, 7.7, and 9.7 dS

m-1, respectively (Fig. 10). The salinity threshold value of

6.9 dS m-1 for wheat (Wahedi 1995) is never reached over

the entire analyzed time period when irrigation water with an

ECiw of 4 dS m-1 is used. When irrigation water with an

ECiw of 6 dS m-1 is used, maximum seasonal soil salinity is

maintained below 6.95 dS m-1, which translates into a yield

of over 99.7 % of the optimal production.

Simulations further show that irrigation water with an

ECiw of 8 dS m-1 can be safely used only in above-average

rainfall years. Annual rainfall of 365 mm or more was

found to be adequate to keep soil salinity below the

threshold value for wheat. For irrigation water with an

ECiw of 8 dS m-1, an appreciable yield loss of more than

10 % would occur in only 5 years. The maximum yield

reduction of about 13 % would presumably occur in the

2007–2008 year, which had the lowest amount of annual

rainfall on record. The use of irrigation water with a

salinity of 10 dS m-1 would increase soil salinity above the

threshold yield level in all years except in the 2 years

(1986–1987 and 1992–1993) with very high annual rainfall

(449 and 491 mm). The buildup in soil salinity would

cause a loss in the wheat yield ranging from 3 to 26 %.

Results of the probability analysis for the entire time

period of 33 years (Fig. 11) illustrate that all seasonal-

average root-zone salinities are below the threshold value

of 6.9 when irrigation waters with salinities of 4 and 6 dS

m-1 are used. On the other hand, there are only seven and

2 years when the seasonal-average root-zone salinity is

below 6.9 dS m-1 when irrigation waters with an ECiw of 8

and 10 dS m-1 are used, respectively. Given these long-

term simulation results and taking into account all other

factors that may potentially impact the crop yield, using

irrigation water of 6 dS m-1 as a threshold ECiw value for

irrigation water can be considered to be a conservative

choice for cultivating wheat in the Sarvestan area.

ECe=6.9 dS m-1

0

2

4

6

8

10

12

14

1978

-197

9

1980

-198

1

1982

-198

3

1984

-198

5

1986

-198

7

1988

-198

9

1990

-199

1

1992

-199

3

1994

-199

5

1996

-199

7

1998

-199

9

2000

-200

1

2002

-200

3

2004

-200

5

2006

-200

7

2008

-200

9

2010

-201

1

Ave

rage

Roo

t-zo

ne S

alin

ity

(dS

m-1

)

0

200

400

600

800

1000

Rai

nfal

l (m

m)

Rainfall ECiw= 6 dS/mECiw= 10 dS/m ECiw= 4 dS/m

ECiw= 8 dS/m

Fig. 10 Temporal changes insimulated root-zone salinity

when irrigation waters with

ECiw of 2, 4, 6, and 8 dS m-1

are used

0

20

40

60

80

100

0 2 4 6 8 10 12 14 16

Average Root-zone Salinity (dS m-1)

P(

EC

e< E

Ct)

ECiw= 4 dS/m

ECiw= 6 dS/m

ECiw= 8 dS/m

ECiw= 10 dS/m

Fig. 11 The probability distribution function of the root-zone salinity(ECe) being smaller than the wheat yield threshold (ECt) obtained by

simulating the 33-year series with irrigation waters of an ECiw of 4, 6,

8, and 10 dS m-1

Irrig Sci (2013) 31:1009–1024 1021

123

Results of the long-term simulations for a given irriga-

tion water salinity (Fig. 10) showed certain variability in

soil salinity not only for different amounts of annual

rainfall, but also for the same amounts of annual rainfall

when rainfall was differently distributed during the year.

To test this phenomenon, simulations for 2 years

(2005–2006 and 2010–2011) with similar rainfall and

irrigation water with salinity of 6 dS m-1 were carried out

and compared in Fig. 12. Simulated results showed that the

mean seasonal soil salinity was lower (5.1 dS m-1) in

2010–2011 than in 2005–2006 (5.9 dS m-1). Rainfall that

occurred in a short period of the winter of 2010–2011 was

more effective in reducing the salinity of soil in the root

zone than a similar amount of rainfall distributed

throughout the entire year of 2005–2006. Moreover,

changes in soil salinity during the wheat growing season in

2010–2011 were not consistent with those in 2005–2006.

In the early wheat growth stage of 2005–2006, soil salinity

was lower than in 2010–2011. On the other hand, after

February of 2011, soil salinity dropped dramatically and

stayed continuously below levels reached in 2005–2006.

These results suggest that while winter-concentrated

rainfall has an important influence on seasonal root-zone

salinity, initial soil salinity may be sufficiently reduced by

late autumn rainfall. High initial (during wheat germina-

tion) soil salinity is one of the limiting factors for wheat

production in the Fars Province of Iran (Cheraghi and

Rasouli 2008). Autumn rainfall would thus be very effec-

tive for conditions in which the initial level of soil salinity

is so high that it could adversely influence the wheat

establishment. Research conducted in different parts of the

Fars Province has shown that 30–50 mm of rain would be

sufficient to flush salts out of the top 30 cm depth (Cher-

aghi and Rasouli 2008). In the study area, in 28 years of the

studied time period (33 years), rainfall occurred in late

autumn. The amount of rainfall varied between 12 and

343 mm from November 23 to December 23, a

recommended planting date for wheat in the Fars Province.

Meteorological data illustrate that in 24 years, rainfall was

at least 40 mm during the optimum planting time period. In

other words, rainfall is an important potential salt-leaching

factor in the early stages of wheat growth in 73 % of years.

Hence, irrigation with saline water should be delayed after

December 23, because rainfall may improve conditions for

germination and ensure low salinity in the root zone during

the planting month. If there is not enough rainfall to leach

accumulated salts, non-saline water may need to be applied

to leach salts out of the root zone during stand

establishment.

Conclusions

Numerical simulations with the UNSATCHEM model

were used to evaluate the effects of saline water used for

irrigation of wheat on root-zone salinity and wheat yield.

Based on temporal changes in root-zone salinity simulated

for different modes of conjunctive water use, the following

ranking in the order of decreasing preference (in terms of

yield) was obtained: alternate 3LS:3HS, LS:HS, Mix (1:1),

and alternate HS:LS. Optimal planning of conjunctive

irrigation water use should attempt to delay the salinity

stress to later growth stages as much as possible and to

always keep it within permissible limits.

The balance of salt fluxes and salt storage in the soil

profile was affected by the quantity and quality of irrigation

water, the quantity of rainfall, and by dissolution/pre-

cipitation reactions. Rainfall had a significant effect on soil

desalinization. In addition, precipitation reactions removed

salts from the soil solution and lowered the solution con-

centrations in the root zone, especially when less irrigation

water was used.

The maximum irrigation water ECiw that can be used to

maintain soil salinity below the wheat yield threshold of

0

20

40

60

80

100

120

140

160

0

2

4

6

8

10

1 Nov 1 Des 1 Jan 1 Feb 1 Mar 1 Apr 1 May

Rai

nfal

l (m

m)

Ave

rage

Roo

t-zo

ne S

alin

ity

(dS

m-1

)Month

Rainfall (2005-2006) Rainfall (2010-2011)

Soil Salinity (2005-2006) Soil Salinity (2010-2011)

Fig. 12 Simulated averageroot-zone salinity during 2 year

(2005–2006 and 2010–2011)

with similar amounts but

different distributions of annual

rainfall

1022 Irrig Sci (2013) 31:1009–1024

123

6.9 dS m-1 throughout the growing season was calculated

after taking into account different rates of rainfall. Maxi-

mum salinity of irrigation water for a sustainable produc-

tion of wheat was 6.21 and 6.29 dS m-1 for sub-average

and average rainfall years, respectively. For above-average

rainfall years, saline water with an ECiw of up to 8.31 dS

m-1 can be applied without any reduction in wheat yield.

For irrigating wheat on a sustainable basis, long-term

simulations indicate that it is possible to use saline water

with an ECiw of 6.29 dS m-1. Similar types of simulations

as these can be used to find a suitable quality of irrigation

water for different salinity-sensitive crops. This modeling

approach can be valuable not only for policy and decision

makers, but also for irrigation managers. Given these

results and taking into account other factors that can

potentially impact crop yield, the use of 6 dS m-1 as the

threshold ECiw value for irrigation water can be considered

a conservative value for wheat in the study area.

Rainfall distribution also plays a major role in deter-

mining the salinity of the soil in the root zone. The winter-

concentrated rainfall is more effective in reducing soil

salinity than a similar amount of rainfall distributed

throughout the autumn, winter, and spring seasons.

Autumn rainfall would be very effective in reducing the

potentially high initial soil salinity level to levels that

would not adversely influence wheat establishment.

Results of this study can help us predict the salinity of

water, which can be used on a sustainable and long-term

basis for irrigating a clay loam soil. Moreover, the man-

agement option for minimizing root-zone salinity during

the establishment stage by considering the effects of rain-

fall was evaluated. There are different climatic zones in the

Fars Province, in which weather, water quality, and soil

conditions may vary. The long-term simulations can be

effectively used to establish safe salinity limits for irriga-

tion waters for different climatic zones.

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http://www//farsmet@ir

Modeling the effects of saline water use in wheat-cultivated lands using the UNSATCHEM modelAbstractIntroductionMaterials and methodsModel descriptionField experimentsThe UNSATCHEM model setupStatistical evaluationAlternative irrigation scenarios

Results and discussionCalibration and validation of UNSATCHEM for modeling the use of saline water for irrigationScenario 1: Salinization for different modes of conjunctive water useScenario 2: Salinization for different amounts of applied waterScenario 3: Evaluation of the maximum ECiw to maintain the average root-zone salinity below the soil salinity threshold

ConclusionsReferences